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The exception is mean queue length, which shows the same superlinear increase as in the unreliable non- ow-controlled case This graceful decline in TCP's performance under self-similar loads comes at a cost: a disproportionate increase in the consumption of buffer space The sensitive.



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dependence of mean queue length on self-similarity is consistent with previous works [2, 15, 16] showing that queue length distribution decays more slowly for long-range dependent (LRD) sources than for short-range dependent (SRD) sources. The aforementioned traf c-shaping effect of ow-controlled, reliable transport transforming a large le transfer into an on-average ``thin'' packet train (stretching-in-time effect) suggests, in part, why the on=off model has been so successful despite its limitations a principal effect of interaction among traf c sources in an internetworked environment lies in the generation of long packet trains wherein the correlation structure inherent in heavy-tailed le size distributions is suf ciently preserved. The rest of the chapter is organized as follows. In the next two sections, we discuss related work, the network model, and the simulation setup. This is followed by the main section, which explores the effect of le size distribution on traf c selfsimilarity, including the role of the protocol stack, heavy-tailed versus non-heavytailed interarrival time distribution, resource variations, and traf c mixing. We conclude with a discussion of the effect of traf c self-similarity from a performance evaluation perspective, showing its quantitative and qualitative effects with respect to performance measures when both the degree of self-similarity and network resources are varied.





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Start with that list, and see where it goes. Documentation should contain many cross-references to external documentation and to other parts of internal documentation so that you don t write the same information in more than one place. If someone else has written it down, use that person s copy. If different parts of the same documents disagree, which is inevitable over time without great care, a tremendous amount of time can be wasted trying to determine which passage is correct. Documentation should always be marked with a date and version number, with both updated whenever the slightest change is made. There should even be a manual about the documentation itself. This manual should contain standards information for new and existing documentation things like distribution models, update procedures, version numbering standards, formatting, storage standards, and so on. And all system documentation should include update the documentation as one of the steps to perform when a documented component is changed or added. All the while, be sure that you keep things as simple as possible (always remember Key Principle #1!). People will have to read the documentation that you ve written. If it is unreadable, or unusable, then there is no point to having it at all, and you ll have wasted a tremendous amount of your own time and created a great deal of frustration among your audience.

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Since the seminal study of Leland et al. [14], which set the groundwork for considering self-similar network traf c as an important modeling and performance evaluation problem, a string of work has appeared dealing with various aspects of traf c self-similarity [1, 2, 7, 11, 12, 15, 16, 22, 28]. In measurement based work [7, 11, 12, 14, 22, 28], traf c traces from physical network measurements are employed to identify the presence of scale-invariant burstiness, and models are constructed capable of generating synthetic traf c with matching characteristics. These works show that long-range dependence is an ubiquitous phenomenon encompassing both local-area and wide-area network traf c. In the performance evaluation category are works that have evaluated the effect of self-similar traf c on idealized or simpi ed networks [1, 2, 15, 16]. They show that long-range dependent traf c is likely to degrade performance, and a principal result is the observation that queue length distribution under self-similar traf c decays much more slowly than with short-range-dependent sources (e.g., Poisson). We refer the reader to 1 for a comprehensive survey of related works. Our work is an extension of the line of research in the rst category, where we investigate causal mechanisms that may be at play in real networks responsible for generating the self-similarity phenomena observed in diverse networking contexts.

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